Multivariate time series-based train brake system fault detection method

Abstract

The invention provides a multivariate time series-based train brake system fault detection method. The method comprises the following steps: sample data relevant to train brake system fault detectionis collected, a multivariate time series matrix for the sample data is built, sample data for time series segments is extracted from the multivariate time series matrix via use of a sliding time window, the extracted sample data for the time series segments and characteristic data of an abnormal mode are subjected to matching detection, and train brake system fault detection results in the time series segments can be obtained according to matching detection results. The method disclosed in the invention is based on data analysis, machine learning is combined with a multivariate time series excavation algorithm, a sliding time window-based abnormal mode matching algorithm is put forward, faults in existing data can be subjected to monitoring and intelligent diagnosis via mode matching, intrinsic reasons behind exception can be accurately found, and the exception can be well positioned.

Description

technical field [0001] The invention relates to the technical field of train fault detection, in particular to a multivariate time series-based fault detection method for a train braking system. Background technique [0002] At present, the development direction of railway transportation is heavy-duty and high-speed, that is, freight transportation develops heavy-duty transportation, and passenger transportation develops high-speed railway. Heavy-duty transportation refers to the use of single-engine, double-engine or multi-engine high-power internal combustion or electric locomotives under the condition of advanced railway technology and equipment to increase the number of freight trains and greatly increase the traction weight of trains. At present, locomotives with electronically controlled air brakes with information functions such as computer simulation control and network communication are widely used in China. [0003] The new generation of electronically controlled ...

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Problems solved by technology

[0008] The existing analytical model method is based on the precise mathematical model and uses parameter estimation, observer design and equivalence relationship method to monitor the system. This method is highly dependent on the model accuracy and cannot be applied to the synchronous braking system. Scenarios that cannot be accurately described by such mathematical models and signal flow models

[0009] The existing knowledge-based method is applied to the situation where the process experience knowledge of the monitoring object is relatively complete, and the qualitative model is used to obtain the indicators of process monitoring. Through fuzzy reasoning methods, pattern recognition methods, qualitative observers, knowledge observers, qualitative simulation and The neural network method is used for fault diagnosis, but this method cannot establish an accurate model for monitoring objects with incomplete empirical knowledge.

[0010] When the existing data-driven method is used for fault monitoring, the timing and dynamics of sensor monitoring value changes are ignored, and only the correlation between the variables to be monitored is considered. The focus is on whether the monitoring value at each moment exceeds the normal threshold. , but there is no dynamic change trend of the overall observation data within a period of time, ignoring the correlation between the time before and after. Due to the correlation and complexity of the faults of the braking system, when performing fault monitoring, if only based on a certain It is difficult to accurately locate the fault due to the abnormal observation value at a moment

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